Mobile EEG for neurourbanism research - What could possibly go wrong? A critical review with guidelines

Gramann, Klaus

doi:10.1016/j.jenvp.2024.102308

Cited by 2 publications

(5 citation statements)

References 138 publications

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“…Very few studies reported using amplitude thresholds to reject continuous data or channels, employing source estimation, line noise correction, or semi-automatic IC selection. Previous reviews of mobile EEG point toward the potential pitfalls of consumer EEG systems which typically come with low channel counts (Gramann, 2024; Niso et al, 2022). Most studies included in the present report used research-grade hardware with higher channel counts, which offers more flexibility with signal processing choices.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, mobile EEG, like stationary EEG, may be recorded with dry or wet electrodes and active pre-amplification at the electrode site or amplification only at the amplifier. In contrast to high- density stationary EEG, which is often used in research, mobile EEG systems vary considerably in the number of electrodes used, their spatial coverage, and the placement of the amplifier (Bateson et al, 2017; Gramann, 2024; Niso et al, 2022). These aspects of EEG acquisition may influence which preprocessing steps are most appropriate.…”

Section: Discussionmentioning

confidence: 99%

“…One can include common preprocessing choices in P3 analysis which can be identified with the shiny app. Moreover, recommendations can help identify suitable and preprocessing approaches, as well as set-up procedures and EEG systems (Gorjan et al, 2022; Gramann, 2024; Reis et al, 2014; Song & Nordin, 2021; Wascher et al, 2021). If a scaffolding of steps exists, employing a multiverse analysis using data that will not undergo statistical evaluation can determine appropriate options for specific steps.…”

Section: Discussionmentioning

confidence: 99%

“…Given that no standards for EEG P3 preprocessing exist and that limited resources may prevent researchers from conducting extensive multiverse analysis, we should ensure that preprocessing is at least reproducible. Unfortunately, EEG analysis details are not always reported in sufficient detail (Gramann, 2024; Šoškić et al, 2021). Two possible approaches to tackle this problem may be considered.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, robust preprocessing depends on the investigated ERP component (Clayson et al, 2021). Yet so far this has not been investigated for ERPs recorded with mobile EEG, although mobile capture of brain activity is a prerequisite for understanding neural activity in more ecologically valid settings and is becoming increasingly popular (Gramann, 2024; Niso et al, 2022). The P3 is an ERP component extensively studied during whole-body motions like walking.…”

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confidence: 99%

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Preprocessing Choices for P3 Analyses with Mobile EEG: A Systematic Literature Review and Interactive Exploration

Jacobsen,

Kristanto,

Welp

et al. 2024

Preprint

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Preprocessing is necessary to extract meaningful results from electroencephalography (EEG) data. With many possible preprocessing choices, their impact on outcomes is fundamental. While previous studies have explored the effects of preprocessing on stationary EEG data, this research delves into mobile EEG, where complex processing is necessary to address motion artifacts. Specifically, we describe the preprocessing choices studies reported for analyzing the P3 event-related potential (ERP) during walking and standing. A systematic review of 258 studies (Scopus, Web of Science, PubMed) of the P3 during walking, identified 27 studies meeting the inclusion criteria (empirical study, EEG data of healthy humans, P3 during a gait and non-movement condition). Two independent coders extracted preprocessing choices reported in each study. Analysis of preprocessing choices revealed commonalities and differences, such as widespread use of offline filters but limited application of line noise correction (3 out of 27 studies). Notably, 63% of studies involved manual processing steps, and 52% omitted reporting critical parameters for at least one step. All studies employed unique preprocessing strategies. These findings align with stationary EEG preprocessing results, emphasizing the necessity for standardized reporting in mobile EEG research. We implemented an interactive visualization tool (Shiny app) to aid the exploration of the preprocessing landscape. The Shiny app allows users to structure the literature regarding different processing steps and processing combinations, thereby providing a meaningful overview. It is also possible to enter planned or preferred processing methods and compare own choices with the literature. The app thus helps to identify defensible preprocessing choices, specifically with a focus on P3 ERP analyses during standing and walking. It could be utilized to examine how these choices impact P3 results and understand the robustness of various processing options. We hope to increase awareness regarding the potential influence of preprocessing decisions and advocate for comprehensive reporting standards to foster reproducibility in mobile EEG research.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Preprocessing Choices for P3 Analyses with Mobile EEG: A Systematic Literature Review and Interactive Exploration

Jacobsen,

Kristanto,

Welp

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Preprocessing choices for P3 analyses with mobile EEG: A systematic literature review and interactive exploration

Jacobsen,

Kristanto,

Welp

et al. 2024

Psychophysiology

View full text Add to dashboard Cite

Preprocessing is necessary to extract meaningful results from electroencephalography (EEG) data. With many possible preprocessing choices, their impact on outcomes is fundamental. While previous studies have explored the effects of preprocessing on stationary EEG data, this research delves into mobile EEG, where complex processing is necessary to address motion artifacts. Specifically, we describe the preprocessing choices studies reported for analyzing the P3 event‐related potential (ERP) during walking and standing. A systematic review of 258 studies of the P3 during walking, identified 27 studies meeting the inclusion criteria. Two independent coders extracted preprocessing choices reported in each study. Analysis of preprocessing choices revealed commonalities and differences, such as the widespread use of offline filters but limited application of line noise correction (3 of 27 studies). Notably, 59% of studies involved manual processing steps, and 56% omitted reporting critical parameters for at least one step. All studies employed unique preprocessing strategies. These findings align with stationary EEG preprocessing results, emphasizing the necessity for standardized reporting in mobile EEG research. We implemented an interactive visualization tool (Shiny app) to aid the exploration of the preprocessing landscape. The app allows users to structure the literature regarding different processing steps, enter planned processing methods, and compare them with the literature. The app could be utilized to examine how these choices impact P3 results and understand the robustness of various processing options. We hope to increase awareness regarding the potential influence of preprocessing decisions and advocate for comprehensive reporting standards to foster reproducibility in mobile EEG research.

show abstract

Mobile EEG for neurourbanism research - What could possibly go wrong? A critical review with guidelines

Cited by 2 publications

References 138 publications

Preprocessing Choices for P3 Analyses with Mobile EEG: A Systematic Literature Review and Interactive Exploration

Preprocessing Choices for P3 Analyses with Mobile EEG: A Systematic Literature Review and Interactive Exploration

Preprocessing choices for P3 analyses with mobile EEG: A systematic literature review and interactive exploration

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